Springbok Analytics emphasizes results from scientific reports

A study guided by scientists in Springbok Analytics suggests that a new model for machine learning can predict on MRI-machine learning in patients with facioscapulohumeral muscular dystrophy (FSHD).

This study introduces a multiceligent machine learning framework that uses the full body MRI and clinical data to predict regional, muscles, common and functional progress at FSHD.

“In view of the complexity and heterogeneity of FSHD, traditional functional metrics are simply not the best way to measure the disease,” said Silvia Blemker, PhD, senior author and Chief Scientific Officer and co -founder of Springbok, in a press release. “In this study, we showed that muscle MRI in combination with progressive machine learning makes this complexity grasp and feasible and supports more sensitive, predictive and patient-specific study designs.”

FSHD is a progressive, genetic neuromuscular disorder that affects approximately 7,500 people. It is driven by the aberrant expression of the DUX4 protein, which leads to a waste of skeletal muscles and the functional decline.